{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "421f9c2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d54d5532",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.cuda.is_available()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a9fbb713",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 1.1277, -0.4888,  0.3246,  ...,  1.1070, -0.9968, -0.2758],\n",
       "        [ 0.4283,  0.1429, -0.1161,  ..., -0.2848, -0.4157, -0.2016],\n",
       "        [ 0.1869,  0.8984,  1.7150,  ...,  0.3078, -0.6538,  0.1478]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w = torch.empty(3, 500)\n",
    "# w.uniform_(-0.5,0.5)\n",
    "w.normal_(mean=0,std=1)\n",
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1cf207e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor(0.0222), tensor(1.0026))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.mean(w),torch.std(w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "330bc39a",
   "metadata": {},
   "outputs": [],
   "source": [
    "embeding = nn.Embedding(100,10,sparse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2eb8eded",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Tensor"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(embeding(torch.tensor([1,2,3])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a0b237b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([100, 10])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeding.weight.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "34a5160a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1, 2, 3], dtype=torch.int32)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.from_numpy(np.array([1,2,3])).data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2c358a4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.5819, 0.1360, 0.5820, 0.5524, 0.4175],\n",
       "        [0.3461, 0.4909, 0.6783, 0.1261, 0.3297],\n",
       "        [0.7347, 0.6252, 0.7305, 0.5806, 0.7110]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w = torch.empty(3,5)\n",
    "nn.init.uniform_(w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6f3e4eb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.empty(3,5).numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0d461978",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-0.3161,  0.4527,  0.4823,  0.0144, -0.0759],\n",
       "        [-0.2690,  0.2840,  0.1437, -0.4485,  0.4098],\n",
       "        [ 0.0948,  0.4709, -0.0085, -0.0102, -0.3104]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.nn.init.uniform_(w,-0.5,0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "10d1db5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 0.9202, -0.6137,  0.2750,  0.2157,  0.4206],\n",
       "        [-0.3295, -0.3441,  1.9231,  0.0478,  1.0081],\n",
       "        [ 0.4880,  0.1048,  1.6270,  0.3649,  0.6529]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.nn.init.normal_(w,0,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "29bd8f16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "12//5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9800ba1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 5)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randn(3,5)[:2].shape"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "841224e4",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "daaf0238",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[-0.5114,  0.7963, -0.7430, -0.3383, -1.4666],\n",
      "        [-0.9849,  0.8046, -1.4016, -1.8018, -0.2485],\n",
      "        [-1.6087,  0.6662,  1.4797,  0.3778,  1.1348]], requires_grad=True)\n",
      "tensor([[-1.7016,  0.1062, -0.6080,  1.8156, -0.7314],\n",
      "        [-0.9469, -1.0359, -0.7759, -0.0990,  2.2143],\n",
      "        [-0.7771,  0.7102,  0.8587,  0.1030,  3.4696]])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor([[ 0.1587,  0.0920, -0.0180, -0.2872, -0.0980],\n",
       "        [-0.0051,  0.2454, -0.0834, -0.2270, -0.3284],\n",
       "        [-0.1109, -0.0059,  0.0828,  0.0366, -0.3113]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "模拟mse求导过程\n",
    "\n",
    "\n",
    "'''\n",
    "\n",
    "import torch \n",
    "\n",
    "\n",
    "input = torch.randn(3,5,requires_grad=True)\n",
    "print(input)\n",
    "target = torch.randn(3,5)\n",
    "print(target)\n",
    "\n",
    "loss = torch.nn.MSELoss()\n",
    "res = loss(input,target)\n",
    "res.backward()\n",
    "input.grad\n",
    "\n",
    "'''\n",
    "tensor([[-0.5114,  0.7963, -0.7430, -0.3383, -1.4666],\n",
    "        [-0.9849,  0.8046, -1.4016, -1.8018, -0.2485],\n",
    "        [-1.6087,  0.6662,  1.4797,  0.3778,  1.1348]], requires_grad=True)\n",
    "tensor([[-1.7016,  0.1062, -0.6080,  1.8156, -0.7314],\n",
    "        [-0.9469, -1.0359, -0.7759, -0.0990,  2.2143],\n",
    "        [-0.7771,  0.7102,  0.8587,  0.1030,  3.4696]])\n",
    "tensor([[ 0.1587,  0.0920, -0.0180, -0.2872, -0.0980],\n",
    "        [-0.0051,  0.2454, -0.0834, -0.2270, -0.3284],\n",
    "        [-0.1109, -0.0059,  0.0828,  0.0366, -0.3113]])\n",
    "\n",
    "(input-target)*2/15\n",
    "\n",
    "tensor([[ 0.1587,  0.0920, -0.0180, -0.2872, -0.0980],\n",
    "        [-0.0051,  0.2454, -0.0834, -0.2270, -0.3284],\n",
    "        [-0.1109, -0.0059,  0.0828,  0.0366, -0.3113]], grad_fn=<DivBackward0>)\n",
    "\n",
    "'''\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "e3725e33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 0.1587,  0.0920, -0.0180, -0.2872, -0.0980],\n",
       "        [-0.0051,  0.2454, -0.0834, -0.2270, -0.3284],\n",
       "        [-0.1109, -0.0059,  0.0828,  0.0366, -0.3113]], grad_fn=<DivBackward0>)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(input-target)*2/15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "77ca31a0",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Tensor' object has no attribute 'parements'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[51], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mres\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparements\u001b[49m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'Tensor' object has no attribute 'parements'"
     ]
    }
   ],
   "source": [
    "res.parements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1e70b65",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "2f58614a",
   "metadata": {},
   "source": [
    "cat 连接 tensor\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "f31b2c79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 1.1305,  0.8672,  0.5696],\n",
       "        [-1.2467,  0.5944,  0.2713],\n",
       "        [ 1.1305,  0.8672,  0.5696],\n",
       "        [-1.2467,  0.5944,  0.2713],\n",
       "        [ 1.1305,  0.8672,  0.5696],\n",
       "        [-1.2467,  0.5944,  0.2713]])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 展开\n",
    "w = torch.tensor([1,2,3])\n",
    "w\n",
    "torch.cat([w,w],0)\n",
    "\n",
    "\n",
    "x = torch.randn(2, 3)\n",
    "torch.concat((x,x,x),0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f697813",
   "metadata": {},
   "source": [
    "# transpose的使用原理\n",
    "\n",
    "维度的转换，和reshape有本质的区别，reshape无法转换回去\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7ea404ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.91021164,  0.90413638],\n",
       "       [ 1.49209432,  0.15712242],\n",
       "       [-0.5646881 ,  0.03934224],\n",
       "       [ 1.1427099 , -1.36362343],\n",
       "       [-0.37050565, -0.05354308]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value = np.random.randn(10).reshape(5,2)\n",
    "value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "197fc867",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.91021164,  1.49209432, -0.5646881 ,  1.1427099 , -0.37050565],\n",
       "       [ 0.90413638,  0.15712242,  0.03934224, -1.36362343, -0.05354308]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value.transpose(1,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "844a6d24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[-1.191886  ,  1.47745088, -0.96835256],\n",
       "        [ 1.80008782,  0.78203977, -2.11766241]],\n",
       "\n",
       "       [[-0.33479556, -0.95784423, -1.29077159],\n",
       "        [ 0.72068481,  1.25058585,  0.48175308]],\n",
       "\n",
       "       [[-1.43310141, -0.40006375, -0.89981823],\n",
       "        [ 0.33918135,  0.8105814 , -0.3969994 ]],\n",
       "\n",
       "       [[ 0.49313487, -0.04444457, -0.46200719],\n",
       "        [ 0.52817428, -0.94532245, -1.1130539 ]],\n",
       "\n",
       "       [[ 0.99134621,  0.304339  , -0.31667493],\n",
       "        [ 1.05889236,  1.42287474,  1.01004841]]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value = np.random.randn(30).reshape(5,2,3)\n",
    "value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "cef3321e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[-1.191886  , -0.33479556, -1.43310141,  0.49313487,\n",
       "          0.99134621],\n",
       "        [ 1.80008782,  0.72068481,  0.33918135,  0.52817428,\n",
       "          1.05889236]],\n",
       "\n",
       "       [[ 1.47745088, -0.95784423, -0.40006375, -0.04444457,\n",
       "          0.304339  ],\n",
       "        [ 0.78203977,  1.25058585,  0.8105814 , -0.94532245,\n",
       "          1.42287474]],\n",
       "\n",
       "       [[-0.96835256, -1.29077159, -0.89981823, -0.46200719,\n",
       "         -0.31667493],\n",
       "        [-2.11766241,  0.48175308, -0.3969994 , -1.1130539 ,\n",
       "          1.01004841]]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5,2,3 -> 3,2,5\n",
    "w1 = value.transpose(2,1,0)\n",
    "w1.shape\n",
    "w1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "74ea4dfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.50054613, -2.10612212],\n",
       "       [ 1.93413267,  2.34125293],\n",
       "       [ 0.01527788, -0.69211657],\n",
       "       [ 1.29019707, -1.12864007],\n",
       "       [ 0.07770102,  0.46612931]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value = np.random.randn(10).reshape(5,2)\n",
    "value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e39f541",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.50054613, -2.10612212,  1.93413267,  2.34125293,  0.01527788],\n",
       "       [-0.69211657,  1.29019707, -1.12864007,  0.07770102,  0.46612931]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value.reshape(2,5)  # 先展开 在依次填充到（2,5）的矩阵中"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c08b50d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ba91046",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "8cdaa165",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0.88681643, 0.83774131, 0.70154141, 0.68514463, 0.87879976],\n",
       "        [0.83091477, 0.41730975, 0.80502098, 0.21538237, 0.20973352],\n",
       "        [0.8199743 , 0.40674449, 0.36574589, 0.28134803, 0.95788822]]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w = np.random.random(size=(3,5))\n",
    "w[np.newaxis,:,:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d7fc6e0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "6b8526ae",
   "metadata": {},
   "source": [
    "avg_pool1d\n",
    "最后一个维度进行平均池化\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "ad1b390b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 0.5516, -0.8773],\n",
       "        [ 0.1402, -0.4884],\n",
       "        [-0.1079,  0.3708]])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v = torch.randn(size=(3,10))\n",
    "v\n",
    "F.avg_pool1d(v,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "1a31e7a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# AG_NEWS数据集"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59529f8c",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "89d66b0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ShardingFilterIterDataPipe ShardingFilterIterDataPipe\n"
     ]
    }
   ],
   "source": [
    "from torchtext.datasets import AG_NEWS\n",
    "import pathlib\n",
    "now_folder = r'C:\\Users\\caofei\\Desktop\\torch1\\新闻分类\\案例1\\datasets\\AG News\\data'\n",
    "# train_iter, test_iter = AG_NEWS(root=r'D:\\Datasets\\AG News\\data')\n",
    "train_iter, test_iter = AG_NEWS(root=now_folder)\n",
    "print(train_iter,test_iter)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "6aa54f9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torchtext.datasets import AG_NEWS\n",
    "train_iter = AG_NEWS(split='train')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "4e743f14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ShardingFilterIterDataPipe"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_iter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "607c87a8",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'ShardingFilterIterDataPipe' object is not an iterator",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[111], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtrain_iter\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[1;31mTypeError\u001b[0m: 'ShardingFilterIterDataPipe' object is not an iterator"
     ]
    }
   ],
   "source": [
    "next(train_iter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "dbdbbdd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchtext.data.utils import get_tokenizer\n",
    "from torchtext.vocab import build_vocab_from_iterator\n",
    "\n",
    "tokenizer = get_tokenizer('basic_english')\t# 基本的英文分词器\n",
    "train_iter = AG_NEWS(split='train',root=now_folder)\t# 训练数据迭代器\n",
    "\n",
    "# 分词生成器\n",
    "def yield_tokens(data_iter):\n",
    "    for _, text in data_iter:\n",
    "        yield tokenizer(text)\n",
    "\n",
    "# 构建词汇表\n",
    "vocab = build_vocab_from_iterator(yield_tokens(train_iter), specials=[\"<unk>\"])\n",
    "# 设置默认索引，当某个单词不在词汇表中，则返回0\n",
    "vocab.set_default_index(vocab[\"<unk>\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "db5bb176",
   "metadata": {},
   "outputs": [],
   "source": [
    "import jieba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "657732dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Building prefix dict from the default dictionary ...\n",
      "Dumping model to file cache C:\\Users\\caofei\\AppData\\Local\\Temp\\jieba.cache\n",
      "Loading model cost 1.342 seconds.\n",
      "Prefix dict has been built successfully.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['i', ' ', 'like', ' ', ' ', 'cat']"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer(\"i like  cat\")\n",
    "jieba.lcut(\"i like  cat\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "36c574b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Vocab()"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "build_vocab_from_iterator(['i', 'like', 'cat'],specials=[\"<unk>\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "e0dc26cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[475, 21, 30, 5297]"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab(['here', 'is', 'an', 'example'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9d42ba1",
   "metadata": {},
   "outputs": [],
   "source": [
    "text_pipeline = lambda x: vocab(tokenizer(x))\n",
    "label_pipeline = lambda x: int(x) - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f4cf6f5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b986b024",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "# 使用GPU\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "# 定义collate_batch函数，在DataLoader中会使用，对传入的样本数据进行批量处理\n",
    "def collate_batch(batch):\n",
    "\t# 存放label以及text的列表，offses存放每条text的偏移量\n",
    "    label_list, text_list, offsets = [], [], [0]\n",
    "    for (_label, _text) in batch:\n",
    "         label_list.append(label_pipeline(_label))\n",
    "         processed_text = torch.tensor(text_pipeline(_text), dtype=torch.int64)\n",
    "         text_list.append(processed_text)\n",
    "         # 将每一条数据的长度放入offsets列表当中\n",
    "         offsets.append(processed_text.size(0))\n",
    "    label_list = torch.tensor(label_list, dtype=torch.int64)\n",
    "    # 计算出每一条text的偏移量\n",
    "    offsets = torch.tensor(offsets[:-1]).cumsum(dim=0)\n",
    "    text_list = torch.cat(text_list)\n",
    "    return label_list.to(device), text_list.to(device), offsets.to(device)\n",
    "\n",
    "train_iter = AG_NEWS(split='train')\n",
    "dataloader = DataLoader(train_iter, batch_size=8, shuffle=False, collate_fn=collate_batch)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "82f8ed68",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1525ffe0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4df33169",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "67db35af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, \"Wall St. Bears Claw Back Into the Black (Reuters) Reuters - Short-sellers, Wall Street's dwindling\\\\band of ultra-cynics, are seeing green again.\")\n"
     ]
    }
   ],
   "source": [
    "for _ in train_iter:\n",
    "    print(_)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "b88ba9fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\VirtualProject\\Python37Env\\torch_py38\\lib\\site-packages\\torch\\utils\\data\\datapipes\\iter\\combining.py:333: UserWarning: Some child DataPipes are not exhausted when __iter__ is called. We are resetting the buffer and each child DataPipe will read from the start again.\n",
      "  warnings.warn(\"Some child DataPipes are not exhausted when __iter__ is called. We are resetting \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(3,\n",
       " \"Wall St. Bears Claw Back Into the Black (Reuters) Reuters - Short-sellers, Wall Street's dwindling\\\\band of ultra-cynics, are seeing green again.\")"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w = next(iter(train_iter))\n",
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "70bc339c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Wall St. Bears Claw Back Into the Black (Reuters) Reuters - Short-sellers, Wall Street's dwindling\\\\band of ultra-cynics, are seeing green again.\""
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w[0]\n",
    "w[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "7eeedf41",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "test_dataloader = DataLoader(test_iter,batch_size=16,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "74d40194",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[tensor([4, 2, 1, 4, 2, 2, 3, 3, 4, 3, 2, 3, 2, 1, 1, 3]),\n",
       " (\"Microsoft Japan to give away over one million XP SP2 CDs TOKYO -- Microsoft Corp.'s Japanese arm will begin giving away more than a million CD-ROMs of the company's latest security update, Windows XP Service Pack 2 (SP2) from 27,500 locations during September and October, the company said on Thursday.\",\n",
       "  'IAAF to increase anti-doping measures The IAAF will increase testing and funding as well as cooperation with the World Anti-Doping Agency in its bid to detect and stem the use of new performance-enhancing substances, the sport #39;s governing body said Sunday.',\n",
       "  'At Least 24 Killed Morocco Bush Crash (AP) AP - A bus, truck and taxi collided in a mountainous region of western Morocco Saturday, killing 24 people and injuring about 20 others, the official MAP news agency reported.',\n",
       "  'Linux Promoters Challenge Microsoft (AP) AP - Seeking to be more competitive with Microsoft Corp., Linux backers have agreed on a standard version of the operating system so that programs written for one Linux distribution will work with others.',\n",
       "  'Astros exercise Biggio #39;s option, but not Kent #39;s Houston, TX (Sports Network) - Craig Biggio will be around for an 18th season with the Houston Astros. On Thursday, the team exercised its contract option on Biggio for the 2005 season.',\n",
       "  'Sugar Shane Not so Sweet (November 21, 2004). Ronald Winky Wright successfully defended his 154 lb. title for the sixth time in the highly anticipated rematch with the formerly sweet Sugar Shane Mosley ',\n",
       "  \"WTO Hits EU Again Over Sugar Sales  GENEVA (Reuters) - The World Trade Organization (WTO) has  again declared some European Union sugar exports illegal,  dealing a new blow to the bloc's lavish system of farm  subsidies, a trade source close to the case said Wednesday.\",\n",
       "  'Holiday-Shopping Season Remains Sluggish (Reuters) Reuters - U.S. shoppers have kept a tight grip\\\\on their wallets this holiday season with indices on Tuesday\\\\showing sluggish sales in the second week of the  season.',\n",
       "  'Sun, Microsoft Clause Singles Out Openoffice Sun Microsystems (Quote, Chart) may have saved itself from years of costly litigation when it settled with Microsoft over their long-running Java dispute, but a clause in the landmark deal has open source supporters parsing its potential impact.',\n",
       "  'Holiday sales results lift Wal-Mart, Kmart Wal-Mart Stores Inc. said a surge in after-Christmas shopping spurred December same-store sales gains of about 3 percent, at the high end of its forecast. Kmart Holding Corp. said profit rose 10 percent during the holiday season after it limited deep discounts.',\n",
       "  'Kederis proclaims innocence Olympic champion Kostas Kederis today left hospital ahead of his date with IOC inquisitors claiming his innocence and vowing:  quot;After the crucifixion comes the resurrection. quot; ...',\n",
       "  'Poultry Stocks See Mixed Recovery Despite a weak third-quarter earnings report that sent its shares plunging 24 percent Tuesday, poultry producer Sanderson Farms Inc.',\n",
       "  'Rijkaard - has suffered Edmilson blow Barcelona have been stunned by the news that Brazilian centre-half Edmilson will be out of action for at least six months with a knee injury.',\n",
       "  'Austria to Extradite Turkish Underworld Figure  VIENNA (Reuters) - Convicted Turkish underworld boss  Alaattin Cakici, sought on charges of corruption and extortion,  will be extradited from Austria to Turkey, a district court  ruled on Monday.',\n",
       "  \"Congress Members Seek Officer's Dismissal (AP) AP - A group of congressional Democrats is asking President Bush to dismiss a senior military intelligence officer who made church speeches that included inflammatory religious remarks while discussing the war on terrorism.\",\n",
       "  'Update 1: Sysco Profit Climbs 8 Percent on Sales Sysco Corp., the country #39;s largest food service distributor, Monday said profit for its latest quarter rose 8 percent, as it increased sales and trimmed expenses despite the hurricanes in the Southeast.')]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "next(iter(test_dataloader))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "b188f302",
   "metadata": {},
   "outputs": [],
   "source": [
    "def genrate_batch(batch):\n",
    "    label = [_[0] for _ in batch]\n",
    "    label = torch.tensor(label)\n",
    "    text = [_[1] for _ in batch]\n",
    "    text = torch.cat(text)\n",
    "    return text,label\n",
    "\n",
    "test_dataloader = DataLoader(test_iter,batch_size=16,shuffle=True,collate_fn=genrate_batch)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "2df6f97b",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "expected Tensor as element 0 in argument 0, but got str",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[95], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43miter\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtest_dataloader\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32md:\\VirtualProject\\Python37Env\\torch_py38\\lib\\site-packages\\torch\\utils\\data\\dataloader.py:630\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    627\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    628\u001b[0m     \u001b[38;5;66;03m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[0;32m    629\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset()  \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n\u001b[1;32m--> 630\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_next_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    631\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m    632\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[0;32m    633\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[0;32m    634\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called:\n",
      "File \u001b[1;32md:\\VirtualProject\\Python37Env\\torch_py38\\lib\\site-packages\\torch\\utils\\data\\dataloader.py:674\u001b[0m, in \u001b[0;36m_SingleProcessDataLoaderIter._next_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    672\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_next_data\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    673\u001b[0m     index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_next_index()  \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[1;32m--> 674\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dataset_fetcher\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfetch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# may raise StopIteration\u001b[39;00m\n\u001b[0;32m    675\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory:\n\u001b[0;32m    676\u001b[0m         data \u001b[38;5;241m=\u001b[39m _utils\u001b[38;5;241m.\u001b[39mpin_memory\u001b[38;5;241m.\u001b[39mpin_memory(data, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory_device)\n",
      "File \u001b[1;32md:\\VirtualProject\\Python37Env\\torch_py38\\lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py:42\u001b[0m, in \u001b[0;36m_IterableDatasetFetcher.fetch\u001b[1;34m(self, possibly_batched_index)\u001b[0m\n\u001b[0;32m     40\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m     41\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mnext\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset_iter)\n\u001b[1;32m---> 42\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcollate_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[1;32mIn[94], line 5\u001b[0m, in \u001b[0;36mgenrate_batch\u001b[1;34m(batch)\u001b[0m\n\u001b[0;32m      3\u001b[0m label \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mtensor(label)\n\u001b[0;32m      4\u001b[0m text \u001b[38;5;241m=\u001b[39m [_[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m batch]\n\u001b[1;32m----> 5\u001b[0m text \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcat\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtext\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      6\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m text,label\n",
      "\u001b[1;31mTypeError\u001b[0m: expected Tensor as element 0 in argument 0, but got str"
     ]
    }
   ],
   "source": [
    "next(iter(test_dataloader))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "1ccb3bf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.utils.data.dataloader.DataLoader"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(test_dataloader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f82bb796",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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